article · Digital Commons - USU (Utah State University) · 2019

Details on O-SBL(MCMC): A Compressive Sensing Algorithm for Sparse Signal Recovery for the SMV/MMV Problem Using Sparse Bayesian Learning and Markov Chain Monte Carlo Inference

Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Cited by 0Open access

Abstract

This report provides details on O-SBL(MCMC) algorithm for the recovery of jointly-sparse signals for the multiple measurement vector (MMV) problem. For the MMVs with this structure, the solution matrix, which is a collection of sparse vectors, is expected to exhibit joint sparsity across the columns. The notion of joint sparsity here means that the columns of the solution matrix share common supports. This algorithm employs a sparse Bayesian learning (SBL) model to encourage the joint sparsity structure across the columns of the solution. While the proposed algorithm is constructed for the MMV problems, it can also be applied to the single measurement vector (SMV) problems. Part of this work has been published in [1, 2].

Cite this paper

APA 7

Shekaramiz, M., Moon, T. K., & Gunther, J. H. (2019). Details on O-SBL(MCMC): A Compressive Sensing Algorithm for Sparse Signal Recovery for the SMV/MMV Problem Using Sparse Bayesian Learning and Markov Chain Monte Carlo Inference. Digital Commons - USU (Utah State University), 1. https://doi.org/10.13140/rg.2.2.12161.30561

MLA 9

Shekaramiz, Mohammad, et al. “Details on O-SBL(MCMC): A Compressive Sensing Algorithm for Sparse Signal Recovery for the SMV/MMV Problem Using Sparse Bayesian Learning and Markov Chain Monte Carlo Inference.” Digital Commons - USU (Utah State University), 2019, p. 1, https://doi.org/10.13140/rg.2.2.12161.30561.

Chicago

Shekaramiz, Mohammad, Todd K. Moon, and Jacob H. Gunther. 2019. “Details on O-SBL(MCMC): A Compressive Sensing Algorithm for Sparse Signal Recovery for the SMV/MMV Problem Using Sparse Bayesian Learning and Markov Chain Monte Carlo Inference.” Digital Commons - USU (Utah State University): 1. https://doi.org/10.13140/rg.2.2.12161.30561.

Harvard

Shekaramiz, M., Moon, T.K. and Gunther, J.H. (2019) ‘Details on O-SBL(MCMC): A Compressive Sensing Algorithm for Sparse Signal Recovery for the SMV/MMV Problem Using Sparse Bayesian Learning and Markov Chain Monte Carlo Inference’, Digital Commons - USU (Utah State University), p. 1. Available at: https://doi.org/10.13140/rg.2.2.12161.30561.

BibTeX

@article{shekaramiz2019details,
  title = {{Details on O-SBL(MCMC): A Compressive Sensing Algorithm for Sparse Signal Recovery for the SMV/MMV Problem Using Sparse Bayesian Learning and Markov Chain Monte Carlo Inference}},
  author = {Shekaramiz, Mohammad and Moon, Todd K. and Gunther, Jacob H.},
  journal = {Digital Commons - USU (Utah State University)},
  pages = {1},
  year = {2019},
  doi = {10.13140/rg.2.2.12161.30561}
}

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